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Potential Uses in Breadth

  • Michael K. Bergman
Chapter

Abstract

This chapter overviews a dozen knowledge representation (KR) possibilities in breadth. Four potential near-term applications are word sense disambiguation, relation extraction, reciprocal mapping, and extreme knowledge supervision. Word sense disambiguation applied to new domains needs to overcome what is known as the knowledge acquisition bottleneck, which is the cost of finding, structuring, or annotating knowledge for WSD and other natural language processing applications. Relation extraction was also one of the first applications of the use of knowledge bases to inform labeled examples, what we now call distant supervision, which remains one of the better performing methods. All aspects of extracting, identifying, reconciling, and organizing the relation triple may be improved by KBpedia, which also should lead to new capabilities in ontology learning and better capabilities in question answering and data mining. We next cover four logics and representations in automatic hypothesis generation, encapsulating KBpedia for deep learning, measuring classifier performance, and the thermodynamics of representation itself. The last four areas include new applications and uses for knowledge graphs. Two of these, self-service business intelligence and semantic learning, have been on wish lists for years. The last two overviews apply Peirce’s ideas and guidance to nature and questions of the natural world. The examples in this chapter show the benefits of organizing our knowledge structures using Peirce’s universal categories and typologies. Further, with its graph structures and inherent connectedness, we also have some exciting graph learning methods that we can apply to KBpedia and its knowledge bases.

Keywords

Applications Word sense disambiguation Information extraction Knowledge supervision 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Michael K. Bergman
    • 1
  1. 1.Cognonto CorporationCoralvilleUSA

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